{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.658839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.401338</td>\n",
       "      <td>27.306435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10.842809</td>\n",
       "      <td>22.132410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.244147</td>\n",
       "      <td>21.169841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11.645449</td>\n",
       "      <td>15.192634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>12.086957</td>\n",
       "      <td>26.398951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>12.048829</td>\n",
       "      <td>17.435307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>12.889632</td>\n",
       "      <td>25.507885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>13.290970</td>\n",
       "      <td>36.884595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>13.732441</td>\n",
       "      <td>39.666109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>14.133779</td>\n",
       "      <td>34.396281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>14.635117</td>\n",
       "      <td>41.497994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>14.978589</td>\n",
       "      <td>44.981575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>15.377926</td>\n",
       "      <td>47.039595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15.779264</td>\n",
       "      <td>48.252578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16.220736</td>\n",
       "      <td>57.034251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>16.622074</td>\n",
       "      <td>51.490919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>17.023411</td>\n",
       "      <td>51.336621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>17.464883</td>\n",
       "      <td>57.681998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>17.866221</td>\n",
       "      <td>68.553714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>18.267559</td>\n",
       "      <td>64.310925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>18.709030</td>\n",
       "      <td>68.959009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>19.110368</td>\n",
       "      <td>74.614639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>19.511706</td>\n",
       "      <td>71.867195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>19.913043</td>\n",
       "      <td>76.098135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>20.354515</td>\n",
       "      <td>75.775216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>20.755853</td>\n",
       "      <td>72.486055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>21.167191</td>\n",
       "      <td>77.355021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>21.598662</td>\n",
       "      <td>72.118790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>80.260571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Education     Income\n",
       "0    1  10.000000  26.658839\n",
       "1    2  10.401338  27.306435\n",
       "2    3  10.842809  22.132410\n",
       "3    4  11.244147  21.169841\n",
       "4    5  11.645449  15.192634\n",
       "5    6  12.086957  26.398951\n",
       "6    7  12.048829  17.435307\n",
       "7    8  12.889632  25.507885\n",
       "8    9  13.290970  36.884595\n",
       "9   10  13.732441  39.666109\n",
       "10  11  14.133779  34.396281\n",
       "11  12  14.635117  41.497994\n",
       "12  13  14.978589  44.981575\n",
       "13  14  15.377926  47.039595\n",
       "14  15  15.779264  48.252578\n",
       "15  16  16.220736  57.034251\n",
       "16  17  16.622074  51.490919\n",
       "17  18  17.023411  51.336621\n",
       "18  19  17.464883  57.681998\n",
       "19  20  17.866221  68.553714\n",
       "20  21  18.267559  64.310925\n",
       "21  22  18.709030  68.959009\n",
       "22  23  19.110368  74.614639\n",
       "23  24  19.511706  71.867195\n",
       "24  25  19.913043  76.098135\n",
       "25  26  20.354515  75.775216\n",
       "26  27  20.755853  72.486055\n",
       "27  28  21.167191  77.355021\n",
       "28  29  21.598662  72.118790\n",
       "29  30  22.000000  80.260571"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"dataset/income.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x165e94c40>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4sr1Sm1N35qILRUnqs2XPwDPzQGbeVT3+MfAgMAVcCuys3rYT2NxSjZ1xoShJfTbQKJSIWA9sAvYAazPzQPXSE8DaJb5nK7AV4KyzzlpxoV1woShJfRaZWe+NES8Avgr8ZWbuiohDmTl51OtPZeYx++DT09M5MzOzmnolaexExN7MnF64vdYolIg4Afg8cH1m7qo2PxkR66rX1wEHmypWkrS8OqNQArgWeDAzP3XUS7cCW6rHW4Bbmi9PkrSUOj3w84E/Bu6NiLurbR8GtgM3RsTlwKPA21qpUJK0qGUDPDO/BsQSL1/YbDmjz6n5kpoydmuhdMmVECU1aeym0nfJqfmSmmSAD5FT8yU1yQAfokFWQpSk5RjgQ+TUfElN8iLmEDk1X1KTDPCG1B0eWHclRElajgHeAIcHSuqCPfAGODxQUhcM8AY4PFBSFwzwBjg8UFIXDPAGODxQUhe8iNkAhwdK6oIB3hCHB0oaNlsoklQoA1ySCmWAS1KhDHBJKpQBLkmFiswc3odFzDF/A+SVOA34QYPldGlU9mVU9gPcl74alX1Z7X78amauWbhxqAG+GhExk5nTXdfRhFHZl1HZD3Bf+mpU9qWt/bCFIkmFMsAlqVAlBfiOrgto0Kjsy6jsB7gvfTUq+9LKfhTTA5ckPVtJZ+CSpKMY4JJUqF4GeET8fUQcjIj7jtp2SkTsjoj91deTu6yxriX25ZqI+FZEfDMivhARkx2WWMti+3HUax+MiIyI07qobVBL7UtEvKf6e7k/Ij7RVX2DWOLf17kR8fWIuDsiZiLivC5rrCMizoyIOyLiger//3ur7cUd98fYl8aP+14GOHAdcMmCbVcBt2XmS4HbqucluI7n7stu4JWZ+ZvAfwBXD7uoFbiO5+4HEXEm8Cbge8MuaBWuY8G+RMQbgEuBczLzN4BPdlDXSlzHc/9ePgF8LDPPBT5SPe+7p4EPZuYrgNcA746IV1Dmcb/UvjR+3PcywDPz34EfLdh8KbCzerwT2DzMmlZqsX3JzH/NzKerp18Hzhh6YQNa4u8E4NPAh4BiroYvsS/vArZn5k+r9xwcemErsMS+JPDL1eMXAY8PtagVyMwDmXlX9fjHwIPAFAUe90vtSxvHfS8DfAlrM/NA9fgJYG2XxTToT4F/6bqIlYiIS4HZzLyn61oa8DLgdyJiT0R8NSJ+q+uCVuF9wDUR8X3mf5Mo4Te8X4iI9cAmYA+FH/cL9uVojRz3JQX4L+T82MdizviWEhF/zvyvW9d3XcugIuL5wIeZ/xV9FBwPnML8r7xXADdGRHRb0oq9C3h/Zp4JvB+4tuN6aouIFwCfB96Xmf919GulHfdL7UuTx31JAf5kRKwDqL4W8SvuUiLincCbgXdkmYPxfx3YANwTEY8w/+vgXRHxK51WtXKPAbty3jeA/2N+AaISbQF2VY//Gej9RUyAiDiB+cC7PjOP1F/kcb/EvjR+3JcU4Lcy/w+T6ustHdayKhFxCfN947dk5v90Xc9KZOa9mfnizFyfmeuZD8BXZeYTHZe2UjcDbwCIiJcBJ1LuKniPA79XPb4A2N9hLbVUv+1cCzyYmZ866qXijvul9qWV4z4ze/cH+CxwAPgZ88FwOXAq81eh9wP/BpzSdZ2r2JeHgO8Dd1d//qbrOleyHwtefwQ4res6V/F3ciLwT8B9wF3ABV3XuYp9eR2wF7iH+d7rq7uus8Z+vI759sg3jzou/qDE4/4Y+9L4ce9UekkqVEktFEnSUQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVKj/B7ucQXFoqrWTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(data.Education, data.Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1, input_shape=(1,)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_5 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1, input_shape=(1,)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"mse\")\n",
    "x = data.Education\n",
    "y = data.Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1143.7252\n",
      "Epoch 2/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1142.9258\n",
      "Epoch 3/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1142.1267\n",
      "Epoch 4/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1141.3281\n",
      "Epoch 5/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1140.5297\n",
      "Epoch 6/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1139.7319\n",
      "Epoch 7/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1138.9344\n",
      "Epoch 8/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1138.1375\n",
      "Epoch 9/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1137.3409\n",
      "Epoch 10/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1136.5448\n",
      "Epoch 11/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1135.7489\n",
      "Epoch 12/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1134.9536\n",
      "Epoch 13/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1134.1587\n",
      "Epoch 14/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1133.3641\n",
      "Epoch 15/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1132.5702\n",
      "Epoch 16/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1131.7766\n",
      "Epoch 17/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1130.9832\n",
      "Epoch 18/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1130.1902\n",
      "Epoch 19/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1129.3979\n",
      "Epoch 20/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1128.6060\n",
      "Epoch 21/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1127.8143\n",
      "Epoch 22/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1127.0232\n",
      "Epoch 23/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1126.2324\n",
      "Epoch 24/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1125.4420\n",
      "Epoch 25/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1124.6522\n",
      "Epoch 26/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1123.8627\n",
      "Epoch 27/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1123.0735\n",
      "Epoch 28/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1122.2848\n",
      "Epoch 29/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1121.4966\n",
      "Epoch 30/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1120.7087\n",
      "Epoch 31/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1119.9214\n",
      "Epoch 32/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1119.1343\n",
      "Epoch 33/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1118.3478\n",
      "Epoch 34/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1117.5616\n",
      "Epoch 35/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1116.7758\n",
      "Epoch 36/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1115.9904\n",
      "Epoch 37/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1115.2054\n",
      "Epoch 38/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1114.4210\n",
      "Epoch 39/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1113.6368\n",
      "Epoch 40/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1112.8533\n",
      "Epoch 41/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1112.0698\n",
      "Epoch 42/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1111.2870\n",
      "Epoch 43/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1110.5044\n",
      "Epoch 44/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1109.7225\n",
      "Epoch 45/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1108.9409\n",
      "Epoch 46/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1108.1598\n",
      "Epoch 47/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1107.3789\n",
      "Epoch 48/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1106.5985\n",
      "Epoch 49/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1105.8186\n",
      "Epoch 50/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1105.0391\n",
      "Epoch 51/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1104.2600\n",
      "Epoch 52/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1103.4812\n",
      "Epoch 53/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1102.7029\n",
      "Epoch 54/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1101.9250\n",
      "Epoch 55/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1101.1473\n",
      "Epoch 56/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1100.3705\n",
      "Epoch 57/1000\n",
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      "Epoch 984/1000\n",
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      "Epoch 989/1000\n",
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      "Epoch 991/1000\n",
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      "Epoch 992/1000\n",
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      "Epoch 997/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 537.3527\n",
      "Epoch 998/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 536.9174\n",
      "Epoch 999/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 536.4825\n",
      "Epoch 1000/1000\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 536.0478\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x165edb460>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[38.99792]], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(pd.Series([20]))"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  "kernelspec": {
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